---
license: mit
base_model: microsoft/Phi-3-mini-4k-instruct
tags:
- generated_from_trainer
model-index:
- name: V0508HMA15HPHI3B2
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# V0508HMA15HPHI3B2

This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0885

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.3691        | 0.09  | 10   | 0.1863          |
| 0.1586        | 0.18  | 20   | 0.1406          |
| 0.1444        | 0.27  | 30   | 0.1391          |
| 0.1366        | 0.36  | 40   | 0.1237          |
| 0.1228        | 0.45  | 50   | 0.1353          |
| 0.1212        | 0.54  | 60   | 0.0898          |
| 0.1116        | 0.63  | 70   | 0.1008          |
| 0.0983        | 0.73  | 80   | 0.0803          |
| 0.0756        | 0.82  | 90   | 0.0930          |
| 0.0848        | 0.91  | 100  | 0.0721          |
| 0.073         | 1.0   | 110  | 0.0729          |
| 0.0499        | 1.09  | 120  | 0.0691          |
| 0.0594        | 1.18  | 130  | 0.1105          |
| 0.067         | 1.27  | 140  | 0.0751          |
| 0.0489        | 1.36  | 150  | 0.0821          |
| 0.0622        | 1.45  | 160  | 0.0838          |
| 0.0654        | 1.54  | 170  | 0.0764          |
| 0.0574        | 1.63  | 180  | 0.0826          |
| 0.0562        | 1.72  | 190  | 0.0757          |
| 0.0608        | 1.81  | 200  | 0.0795          |
| 0.061         | 1.9   | 210  | 0.0796          |
| 0.0552        | 1.99  | 220  | 0.0796          |
| 0.0293        | 2.08  | 230  | 0.0849          |
| 0.0219        | 2.18  | 240  | 0.1143          |
| 0.0257        | 2.27  | 250  | 0.0967          |
| 0.0204        | 2.36  | 260  | 0.0831          |
| 0.0251        | 2.45  | 270  | 0.0882          |
| 0.0182        | 2.54  | 280  | 0.0959          |
| 0.0189        | 2.63  | 290  | 0.0925          |
| 0.0243        | 2.72  | 300  | 0.0909          |
| 0.0226        | 2.81  | 310  | 0.0890          |
| 0.017         | 2.9   | 320  | 0.0884          |
| 0.0201        | 2.99  | 330  | 0.0885          |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.14.1